# coding=utf-8 # Copyright 2021 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Testing suite for the PyTorch Wav2Vec2 model. """ import math import unittest import pytest from tests.test_modeling_common import floats_tensor, ids_tensor, random_attention_mask from transformers import Wav2Vec2Config, is_torch_available from transformers.testing_utils import require_datasets, require_soundfile, require_torch, slow, torch_device from .test_configuration_common import ConfigTester from .test_modeling_common import ModelTesterMixin, _config_zero_init if is_torch_available(): import torch from transformers import ( Wav2Vec2FeatureExtractor, Wav2Vec2ForCTC, Wav2Vec2ForMaskedLM, Wav2Vec2ForPreTraining, Wav2Vec2ForSequenceClassification, Wav2Vec2Model, Wav2Vec2Processor, ) from transformers.models.wav2vec2.modeling_wav2vec2 import Wav2Vec2GumbelVectorQuantizer, _compute_mask_indices class Wav2Vec2ModelTester: def __init__( self, parent, batch_size=13, seq_length=1024, # speech is longer is_training=False, hidden_size=16, feat_extract_norm="group", feat_extract_dropout=0.0, feat_extract_activation="gelu", conv_dim=(32, 32, 32), conv_stride=(4, 4, 4), conv_kernel=(8, 8, 8), conv_bias=False, num_conv_pos_embeddings=16, num_conv_pos_embedding_groups=2, num_hidden_layers=4, num_attention_heads=2, hidden_dropout_prob=0.1, # this is most likely not correctly set yet intermediate_size=20, layer_norm_eps=1e-5, hidden_act="gelu", initializer_range=0.02, vocab_size=32, do_stable_layer_norm=False, scope=None, ): self.parent = parent self.batch_size = batch_size self.seq_length = seq_length self.is_training = is_training self.hidden_size = hidden_size self.feat_extract_norm = feat_extract_norm self.feat_extract_dropout = feat_extract_dropout self.feat_extract_activation = feat_extract_activation self.conv_dim = conv_dim self.conv_stride = conv_stride self.conv_kernel = conv_kernel self.conv_bias = conv_bias self.num_conv_pos_embeddings = num_conv_pos_embeddings self.num_conv_pos_embedding_groups = num_conv_pos_embedding_groups self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.hidden_dropout_prob = hidden_dropout_prob self.intermediate_size = intermediate_size self.layer_norm_eps = layer_norm_eps self.hidden_act = hidden_act self.initializer_range = initializer_range self.vocab_size = vocab_size self.do_stable_layer_norm = do_stable_layer_norm self.scope = scope output_seq_length = self.seq_length for kernel, stride in zip(self.conv_kernel, self.conv_stride): output_seq_length = (output_seq_length - (kernel - 1)) / stride self.output_seq_length = int(math.ceil(output_seq_length)) self.encoder_seq_length = self.output_seq_length def prepare_config_and_inputs(self): input_values = floats_tensor([self.batch_size, self.seq_length], self.vocab_size) attention_mask = random_attention_mask([self.batch_size, self.seq_length]) config = self.get_config() return config, input_values, attention_mask def get_config(self): return Wav2Vec2Config( hidden_size=self.hidden_size, feat_extract_norm=self.feat_extract_norm, feat_extract_dropout=self.feat_extract_dropout, feat_extract_activation=self.feat_extract_activation, conv_dim=self.conv_dim, conv_stride=self.conv_stride, conv_kernel=self.conv_kernel, conv_bias=self.conv_bias, num_conv_pos_embeddings=self.num_conv_pos_embeddings, num_conv_pos_embedding_groups=self.num_conv_pos_embedding_groups, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, hidden_dropout_prob=self.hidden_dropout_prob, intermediate_size=self.intermediate_size, layer_norm_eps=self.layer_norm_eps, hidden_act=self.hidden_act, initializer_range=self.initializer_range, vocab_size=self.vocab_size, ) def create_and_check_model(self, config, input_values, attention_mask): model = Wav2Vec2Model(config=config) model.to(torch_device) model.eval() result = model(input_values, attention_mask=attention_mask) self.parent.assertEqual( result.last_hidden_state.shape, (self.batch_size, self.output_seq_length, self.hidden_size) ) def create_and_check_batch_inference(self, config, input_values, *args): # test does not pass for models making use of `group_norm` # check: https://github.com/pytorch/fairseq/issues/3227 model = Wav2Vec2Model(config=config) model.to(torch_device) model.eval() input_values = input_values[:3] attention_mask = torch.ones(input_values.shape, device=torch_device, dtype=torch.bool) input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]] # pad input for i in range(len(input_lengths)): input_values[i, input_lengths[i] :] = 0.0 attention_mask[i, input_lengths[i] :] = 0.0 batch_outputs = model(input_values, attention_mask=attention_mask).last_hidden_state for i in range(input_values.shape[0]): input_slice = input_values[i : i + 1, : input_lengths[i]] output = model(input_slice).last_hidden_state batch_output = batch_outputs[i : i + 1, : output.shape[1]] self.parent.assertTrue(torch.allclose(output, batch_output, atol=1e-3)) def check_ctc_loss(self, config, input_values, *args): model = Wav2Vec2ForCTC(config=config) model.to(torch_device) # make sure that dropout is disabled model.eval() input_values = input_values[:3] attention_mask = torch.ones(input_values.shape, device=torch_device, dtype=torch.long) input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]] max_length_labels = model._get_feat_extract_output_lengths(torch.tensor(input_lengths)) labels = ids_tensor((input_values.shape[0], min(max_length_labels) - 1), model.config.vocab_size) # pad input for i in range(len(input_lengths)): input_values[i, input_lengths[i] :] = 0.0 attention_mask[i, input_lengths[i] :] = 0 model.config.ctc_loss_reduction = "sum" sum_loss = model(input_values, attention_mask=attention_mask, labels=labels).loss.item() model.config.ctc_loss_reduction = "mean" mean_loss = model(input_values, attention_mask=attention_mask, labels=labels).loss.item() self.parent.assertTrue(isinstance(sum_loss, float)) self.parent.assertTrue(isinstance(mean_loss, float)) def check_seq_classifier_loss(self, config, input_values, *args): model = Wav2Vec2ForSequenceClassification(config=config) model.to(torch_device) # make sure that dropout is disabled model.eval() input_values = input_values[:3] attention_mask = torch.ones(input_values.shape, device=torch_device, dtype=torch.long) input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]] labels = ids_tensor((input_values.shape[0], 1), len(model.config.id2label)) # pad input for i in range(len(input_lengths)): input_values[i, input_lengths[i] :] = 0.0 attention_mask[i, input_lengths[i] :] = 0 masked_loss = model(input_values, attention_mask=attention_mask, labels=labels).loss.item() unmasked_loss = model(input_values, labels=labels).loss.item() self.parent.assertTrue(isinstance(masked_loss, float)) self.parent.assertTrue(isinstance(unmasked_loss, float)) self.parent.assertTrue(masked_loss != unmasked_loss) def check_ctc_training(self, config, input_values, *args): config.ctc_zero_infinity = True model = Wav2Vec2ForCTC(config=config) model.to(torch_device) model.train() # freeze feature encoder model.freeze_feature_extractor() input_values = input_values[:3] input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]] max_length_labels = model._get_feat_extract_output_lengths(torch.tensor(input_lengths)) labels = ids_tensor((input_values.shape[0], max(max_length_labels) - 2), model.config.vocab_size) # pad input for i in range(len(input_lengths)): input_values[i, input_lengths[i] :] = 0.0 if max_length_labels[i] < labels.shape[-1]: # it's important that we make sure that target lenghts are at least # one shorter than logit lenghts to prevent -inf labels[i, max_length_labels[i] - 1 :] = -100 loss = model(input_values, labels=labels).loss self.parent.assertFalse(torch.isinf(loss).item()) loss.backward() def check_seq_classifier_training(self, config, input_values, *args): config.ctc_zero_infinity = True model = Wav2Vec2ForSequenceClassification(config=config) model.to(torch_device) model.train() # freeze everything but the classification head model.freeze_base_model() input_values = input_values[:3] input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]] labels = ids_tensor((input_values.shape[0], 1), len(model.config.id2label)) # pad input for i in range(len(input_lengths)): input_values[i, input_lengths[i] :] = 0.0 loss = model(input_values, labels=labels).loss self.parent.assertFalse(torch.isinf(loss).item()) loss.backward() def check_labels_out_of_vocab(self, config, input_values, *args): model = Wav2Vec2ForCTC(config) model.to(torch_device) model.train() input_values = input_values[:3] input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]] max_length_labels = model._get_feat_extract_output_lengths(torch.tensor(input_lengths)) labels = ids_tensor((input_values.shape[0], max(max_length_labels) - 2), model.config.vocab_size + 100) with pytest.raises(ValueError): model(input_values, labels=labels) def prepare_config_and_inputs_for_common(self): config, input_values, attention_mask = self.prepare_config_and_inputs() inputs_dict = {"input_values": input_values, "attention_mask": attention_mask} return config, inputs_dict @require_torch class Wav2Vec2ModelTest(ModelTesterMixin, unittest.TestCase): all_model_classes = ( (Wav2Vec2ForCTC, Wav2Vec2Model, Wav2Vec2ForMaskedLM, Wav2Vec2ForSequenceClassification, Wav2Vec2ForPreTraining) if is_torch_available() else () ) test_pruning = False test_headmasking = False test_torchscript = False def setUp(self): self.model_tester = Wav2Vec2ModelTester(self) self.config_tester = ConfigTester(self, config_class=Wav2Vec2Config, hidden_size=37) def test_config(self): self.config_tester.run_common_tests() def test_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*config_and_inputs) def test_ctc_loss_inference(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.check_ctc_loss(*config_and_inputs) def test_seq_classifier_loss_inference(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.check_seq_classifier_loss(*config_and_inputs) def test_ctc_train(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.check_ctc_training(*config_and_inputs) def test_seq_classifier_train(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.check_seq_classifier_training(*config_and_inputs) def test_labels_out_of_vocab(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.check_labels_out_of_vocab(*config_and_inputs) # Wav2Vec2 has no inputs_embeds def test_inputs_embeds(self): pass # `input_ids` is renamed to `input_values` def test_forward_signature(self): pass # Wav2Vec2 cannot resize token embeddings # since it has no tokens embeddings def test_resize_tokens_embeddings(self): pass # Wav2Vec2 has no inputs_embeds # and thus the `get_input_embeddings` fn # is not implemented def test_model_common_attributes(self): pass def test_retain_grad_hidden_states_attentions(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.output_hidden_states = True config.output_attentions = True # no need to test all models as different heads yield the same functionality model_class = self.all_model_classes[0] model = model_class(config) model.to(torch_device) # set layer drop to 0 model.config.layerdrop = 0.0 input_values = inputs_dict["input_values"] input_lengths = torch.tensor( [input_values.shape[1] for _ in range(input_values.shape[0])], dtype=torch.long, device=torch_device ) output_lengths = model._get_feat_extract_output_lengths(input_lengths) labels = ids_tensor((input_values.shape[0], output_lengths[0] - 2), self.model_tester.vocab_size) inputs_dict["attention_mask"] = torch.ones_like(inputs_dict["attention_mask"]) inputs_dict["labels"] = labels outputs = model(**inputs_dict) output = outputs[0] # Encoder-/Decoder-only models hidden_states = outputs.hidden_states[0] attentions = outputs.attentions[0] hidden_states.retain_grad() attentions.retain_grad() output.flatten()[0].backward(retain_graph=True) self.assertIsNotNone(hidden_states.grad) self.assertIsNotNone(attentions.grad) def test_initialization(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() configs_no_init = _config_zero_init(config) for model_class in self.all_model_classes: model = model_class(config=configs_no_init) for name, param in model.named_parameters(): uniform_init_parms = [ "conv.weight", "masked_spec_embed", "codevectors", "quantizer.weight_proj.weight", ] if param.requires_grad: if any([x in name for x in uniform_init_parms]): self.assertTrue( -1.0 <= ((param.data.mean() * 1e9).round() / 1e9).item() <= 1.0, msg=f"Parameter {name} of model {model_class} seems not properly initialized", ) else: self.assertIn( ((param.data.mean() * 1e9).round() / 1e9).item(), [0.0, 1.0], msg=f"Parameter {name} of model {model_class} seems not properly initialized", ) # overwrite from test_modeling_common def _mock_init_weights(self, module): if hasattr(module, "weight") and module.weight is not None: module.weight.data.fill_(3) if hasattr(module, "weight_g") and module.weight_g is not None: module.weight_g.data.fill_(3) if hasattr(module, "weight_v") and module.weight_v is not None: module.weight_v.data.fill_(3) if hasattr(module, "bias") and module.bias is not None: module.bias.data.fill_(3) if hasattr(module, "codevectors") and module.codevectors is not None: module.codevectors.data.fill_(3) if hasattr(module, "masked_spec_embed") and module.masked_spec_embed is not None: module.masked_spec_embed.data.fill_(3) @slow def test_model_from_pretrained(self): model = Wav2Vec2Model.from_pretrained("facebook/wav2vec2-base-960h") self.assertIsNotNone(model) @require_torch class Wav2Vec2RobustModelTest(ModelTesterMixin, unittest.TestCase): all_model_classes = ( (Wav2Vec2ForCTC, Wav2Vec2Model, Wav2Vec2ForMaskedLM, Wav2Vec2ForSequenceClassification, Wav2Vec2ForPreTraining) if is_torch_available() else () ) test_pruning = False test_headmasking = False test_torchscript = False def setUp(self): self.model_tester = Wav2Vec2ModelTester( self, conv_stride=(3, 3, 3), feat_extract_norm="layer", do_stable_layer_norm=True ) self.config_tester = ConfigTester(self, config_class=Wav2Vec2Config, hidden_size=37) def test_config(self): self.config_tester.run_common_tests() def test_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*config_and_inputs) def test_batched_inference(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_batch_inference(*config_and_inputs) def test_ctc_loss_inference(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.check_ctc_loss(*config_and_inputs) def test_seq_classifier_loss_inference(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.check_seq_classifier_loss(*config_and_inputs) def test_ctc_train(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.check_ctc_training(*config_and_inputs) def test_seq_classifier_train(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.check_seq_classifier_training(*config_and_inputs) def test_labels_out_of_vocab(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.check_labels_out_of_vocab(*config_and_inputs) # Wav2Vec2 has no inputs_embeds def test_inputs_embeds(self): pass # `input_ids` is renamed to `input_values` def test_forward_signature(self): pass # Wav2Vec2 cannot resize token embeddings # since it has no tokens embeddings def test_resize_tokens_embeddings(self): pass # Wav2Vec2 has no inputs_embeds # and thus the `get_input_embeddings` fn # is not implemented def test_model_common_attributes(self): pass def test_retain_grad_hidden_states_attentions(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.output_hidden_states = True config.output_attentions = True # no need to test all models as different heads yield the same functionality model_class = self.all_model_classes[0] model = model_class(config) model.to(torch_device) # set layer drop to 0 model.config.layerdrop = 0.0 input_values = inputs_dict["input_values"] input_lengths = torch.tensor( [input_values.shape[1] for _ in range(input_values.shape[0])], dtype=torch.long, device=torch_device ) output_lengths = model._get_feat_extract_output_lengths(input_lengths) labels = ids_tensor((input_values.shape[0], output_lengths[0] - 2), self.model_tester.vocab_size) inputs_dict["attention_mask"] = torch.ones_like(inputs_dict["attention_mask"]) inputs_dict["labels"] = labels outputs = model(**inputs_dict) output = outputs[0] # Encoder-/Decoder-only models hidden_states = outputs.hidden_states[0] attentions = outputs.attentions[0] hidden_states.retain_grad() attentions.retain_grad() output.flatten()[0].backward(retain_graph=True) self.assertIsNotNone(hidden_states.grad) self.assertIsNotNone(attentions.grad) def test_initialization(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() configs_no_init = _config_zero_init(config) for model_class in self.all_model_classes: model = model_class(config=configs_no_init) for name, param in model.named_parameters(): uniform_init_parms = [ "conv.weight", "masked_spec_embed", "codevectors", "quantizer.weight_proj.weight", ] if param.requires_grad: if any([x in name for x in uniform_init_parms]): self.assertTrue( -1.0 <= ((param.data.mean() * 1e9).round() / 1e9).item() <= 1.0, msg=f"Parameter {name} of model {model_class} seems not properly initialized", ) else: self.assertIn( ((param.data.mean() * 1e9).round() / 1e9).item(), [0.0, 1.0], msg=f"Parameter {name} of model {model_class} seems not properly initialized", ) # overwrite from test_modeling_common def _mock_init_weights(self, module): if hasattr(module, "weight") and module.weight is not None: module.weight.data.fill_(3) if hasattr(module, "weight_g") and module.weight_g is not None: module.weight_g.data.fill_(3) if hasattr(module, "weight_v") and module.weight_v is not None: module.weight_v.data.fill_(3) if hasattr(module, "bias") and module.bias is not None: module.bias.data.fill_(3) if hasattr(module, "codevectors") and module.codevectors is not None: module.codevectors.data.fill_(3) if hasattr(module, "masked_spec_embed") and module.masked_spec_embed is not None: module.masked_spec_embed.data.fill_(3) def test_model_for_pretraining(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() model = Wav2Vec2ForPreTraining(config).to(torch_device) features_shape = ( inputs_dict["input_values"].shape[0], model._get_feat_extract_output_lengths(torch.tensor(inputs_dict["input_values"].shape[1])), ) mask_time_indices = _compute_mask_indices( features_shape, model.config.mask_time_prob, model.config.mask_time_length, device=inputs_dict["input_values"].device, min_masks=2, ).to(torch_device) loss = model( inputs_dict["input_values"], attention_mask=inputs_dict["attention_mask"], mask_time_indices=mask_time_indices, ).loss mask_time_indices[:, : mask_time_indices.shape[-1] // 2] = True loss_more_masked = model( inputs_dict["input_values"], attention_mask=inputs_dict["attention_mask"], mask_time_indices=mask_time_indices, ).loss # loss_more_masked has to be bigger or equal loss since more masked inputs have to be predicted self.assertTrue(loss.detach().item() <= loss_more_masked.detach().item()) @slow def test_model_from_pretrained(self): model = Wav2Vec2Model.from_pretrained("facebook/wav2vec2-base-960h") self.assertIsNotNone(model) @require_torch class Wav2Vec2UtilsTest(unittest.TestCase): def test_compute_mask_indices(self): batch_size = 4 sequence_length = 60 mask_prob = 0.5 mask_length = 1 mask = _compute_mask_indices((batch_size, sequence_length), mask_prob, mask_length, torch_device) self.assertListEqual(mask.sum(axis=-1).tolist(), [mask_prob * sequence_length for _ in range(batch_size)]) def test_compute_mask_indices_overlap(self): batch_size = 4 sequence_length = 80 mask_prob = 0.5 mask_length = 4 mask = _compute_mask_indices((batch_size, sequence_length), mask_prob, mask_length, torch_device) # because of overlap mask don't have to add up exactly to `mask_prob * sequence_length`, but have to be smaller or equal for batch_sum in mask.sum(axis=-1): self.assertTrue(int(batch_sum) <= mask_prob * sequence_length) def test_compute_mask_indices_attn_mask_overlap(self): batch_size = 4 sequence_length = 80 mask_prob = 0.5 mask_length = 4 attention_mask = torch.ones((batch_size, sequence_length), dtype=torch.long, device=torch_device) attention_mask[:2, sequence_length // 2 :] = 0 mask = _compute_mask_indices( (batch_size, sequence_length), mask_prob, mask_length, device=torch_device, attention_mask=attention_mask ) for batch_sum in mask.sum(axis=-1): self.assertTrue(int(batch_sum) <= mask_prob * sequence_length) self.assertTrue(mask[:2, sequence_length // 2 :].sum() == 0) def test_compute_perplexity(self): probs = torch.arange(100, device=torch_device).reshape(2, 5, 10) / 100 ppl = Wav2Vec2GumbelVectorQuantizer._compute_perplexity(probs) self.assertTrue(abs(ppl.item() - 141.4291) < 1e-3) # mask half of the input mask = torch.ones((2,), device=torch_device, dtype=torch.bool) mask[0] = 0 ppl = Wav2Vec2GumbelVectorQuantizer._compute_perplexity(probs, mask) self.assertTrue(abs(ppl.item() - 58.6757) < 1e-3) def test_sample_negatives(self): batch_size = 2 sequence_length = 10 hidden_size = 4 num_negatives = 3 features = (torch.arange(sequence_length * hidden_size, device=torch_device) // hidden_size).view( sequence_length, hidden_size ) # each value in vector consits of same value features = features[None, :].expand(batch_size, sequence_length, hidden_size).contiguous() negatives = Wav2Vec2ForPreTraining._sample_negatives(features, num_negatives) self.assertTrue(negatives.shape == (num_negatives, batch_size, sequence_length, hidden_size)) # make sure no negatively sampled vector is actually a positive one for negative in negatives: self.assertTrue(((negative - features) == 0).sum() == 0.0) # make sure that full vectors are sampled and not values of vectors => this means that `unique()` yields a single value for `hidden_size` dim self.assertTrue(negatives.unique(dim=-1).shape, (num_negatives, batch_size, sequence_length, 1)) def test_sample_negatives_with_attn_mask(self): batch_size = 2 sequence_length = 10 hidden_size = 4 num_negatives = 3 # second half of last input tensor is padded attention_mask = torch.ones((batch_size, sequence_length), dtype=torch.long, device=torch_device) attention_mask[-1, sequence_length // 2 :] = 0 features = (torch.arange(sequence_length * hidden_size, device=torch_device) // hidden_size).view( sequence_length, hidden_size ) # each value in vector consits of same value features = features[None, :].expand(batch_size, sequence_length, hidden_size).contiguous() # replace masked feature vectors with -100 to test that those are not sampled features = torch.where(attention_mask[:, :, None].expand(features.shape).bool(), features, -100) negatives = Wav2Vec2ForPreTraining._sample_negatives(features, num_negatives, attention_mask=attention_mask) self.assertTrue((negatives >= 0).all().item()) self.assertTrue(negatives.shape == (num_negatives, batch_size, sequence_length, hidden_size)) # make sure no negatively sampled vector is actually a positive one for negative in negatives: self.assertTrue(((negative - features) == 0).sum() == 0.0) # make sure that full vectors are sampled and not values of vectors => this means that `unique()` yields a single value for `hidden_size` dim self.assertTrue(negatives.unique(dim=-1).shape, (num_negatives, batch_size, sequence_length, 1)) @require_torch @require_datasets @require_soundfile @slow class Wav2Vec2ModelIntegrationTest(unittest.TestCase): def _load_datasamples(self, num_samples): from datasets import load_dataset import soundfile as sf ids = [f"1272-141231-000{i}" for i in range(num_samples)] # map files to raw def map_to_array(batch): speech, _ = sf.read(batch["file"]) batch["speech"] = speech return batch ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation") ds = ds.filter(lambda x: x["id"] in ids).sort("id").map(map_to_array) return ds["speech"][:num_samples] def _load_superb(self, task, num_samples): from datasets import load_dataset ds = load_dataset("anton-l/superb_dummy", task, split="test") return ds[:num_samples] def test_inference_ctc_normal(self): model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-base-960h") model.to(torch_device) processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-base-960h", do_lower_case=True) input_speech = self._load_datasamples(1) input_values = processor(input_speech, return_tensors="pt").input_values.to(torch_device) with torch.no_grad(): logits = model(input_values).logits predicted_ids = torch.argmax(logits, dim=-1) predicted_trans = processor.batch_decode(predicted_ids) EXPECTED_TRANSCRIPTIONS = ["a man said to the universe sir i exist"] self.assertListEqual(predicted_trans, EXPECTED_TRANSCRIPTIONS) def test_inference_ctc_normal_batched(self): model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-base-960h") model.to(torch_device) processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-base-960h", do_lower_case=True) input_speech = self._load_datasamples(2) inputs = processor(input_speech, return_tensors="pt", padding=True) input_values = inputs.input_values.to(torch_device) with torch.no_grad(): logits = model(input_values).logits predicted_ids = torch.argmax(logits, dim=-1) predicted_trans = processor.batch_decode(predicted_ids) EXPECTED_TRANSCRIPTIONS = [ "a man said to the universe sir i exist", "sweat covered brion's body trickling into the tight lowing cloth that was the only garment he wore", ] self.assertListEqual(predicted_trans, EXPECTED_TRANSCRIPTIONS) def test_inference_ctc_robust_batched(self): model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-large-960h-lv60-self").to(torch_device) processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-large-960h-lv60-self", do_lower_case=True) input_speech = self._load_datasamples(4) inputs = processor(input_speech, return_tensors="pt", padding=True) input_values = inputs.input_values.to(torch_device) attention_mask = inputs.attention_mask.to(torch_device) with torch.no_grad(): logits = model(input_values, attention_mask=attention_mask).logits predicted_ids = torch.argmax(logits, dim=-1) predicted_trans = processor.batch_decode(predicted_ids) EXPECTED_TRANSCRIPTIONS = [ "a man said to the universe sir i exist", "sweat covered brion's body trickling into the tight loin cloth that was the only garment he wore", "the cut on his chest still dripping blood the ache of his overstrained eyes even the soaring arena around him with the thousands of spectators were trivialities not worth thinking about", "his instant panic was followed by a small sharp blow high on his chest", ] self.assertListEqual(predicted_trans, EXPECTED_TRANSCRIPTIONS) # Wav2Vec2 pretraining seems to be broken. TODO(PVP) - reenable test once pretraining works # correctly def test_inference_integration(self): return model = Wav2Vec2ForPreTraining.from_pretrained("facebook/wav2vec2-base") model.to(torch_device) feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained( "facebook/wav2vec2-base", return_attention_mask=True ) input_speech = self._load_datasamples(2) inputs_dict = feature_extractor(input_speech, return_tensors="pt", padding=True) features_shape = ( inputs_dict["input_values"].shape[0], model._get_feat_extract_output_lengths(torch.tensor(inputs_dict["input_values"].shape[1])), ) torch.manual_seed(0) mask_time_indices = _compute_mask_indices( features_shape, model.config.mask_time_prob, model.config.mask_time_length, device=inputs_dict["input_values"].device, min_masks=2, ).to(torch_device) with torch.no_grad(): outputs = model( inputs_dict.input_values.to(torch_device), attention_mask=inputs_dict.attention_mask.to(torch_device), mask_time_indices=mask_time_indices, ) # compute cosine similarity cosine_sim = torch.cosine_similarity(outputs.projected_states, outputs.projected_quantized_states, dim=-1) # retrieve cosine sim of masked features cosine_sim_masked = cosine_sim[mask_time_indices] # fmt: off expected_cosine_sim_masked = torch.tensor( [0.7458, 0.7188, 0.6418, 0.3729, 0.3741, 0.3694, 0.3110, 0.2257, 0.4403, 0.5415, 0.3950, 0.3701, 0.8831, 0.8613, 0.5229, 0.6696, 0.7206, 0.7877, 0.6758, 0.8746, 0.6596, 0.6282, 0.6178, 0.5839, 0.5926, 0.6651, 0.4635, 0.6332, 0.6572, 0.8776, 0.4999, 0.7001, 0.7257, 0.5098, 0.6229, 0.4566, 0.5261, 0.6363, 0.5371, 0.6997], device=torch_device, ) # fmt: on self.assertTrue(torch.allclose(cosine_sim_masked, expected_cosine_sim_masked, atol=1e-3)) def test_inference_pretrained(self): model = Wav2Vec2ForPreTraining.from_pretrained("facebook/wav2vec2-base") model.to(torch_device) feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained( "facebook/wav2vec2-base", return_attention_mask=True ) input_speech = self._load_datasamples(2) inputs_dict = feature_extractor(input_speech, return_tensors="pt", padding=True) features_shape = ( inputs_dict["input_values"].shape[0], model._get_feat_extract_output_lengths(torch.tensor(inputs_dict["input_values"].shape[1])), ) torch.manual_seed(0) mask_time_indices = _compute_mask_indices( features_shape, model.config.mask_time_prob, model.config.mask_time_length, device=inputs_dict["input_values"].device, min_masks=2, ).to(torch_device) with torch.no_grad(): outputs = model( inputs_dict.input_values.to(torch_device), attention_mask=inputs_dict.attention_mask.to(torch_device), mask_time_indices=mask_time_indices, ) # compute cosine similarity cosine_sim = torch.cosine_similarity(outputs.projected_states, outputs.projected_quantized_states, dim=-1) # retrieve cosine sim of masked features cosine_sim_masked = cosine_sim[mask_time_indices] # ... now compare to randomly initialized model config = Wav2Vec2Config.from_pretrained("facebook/wav2vec2-base") model_rand = Wav2Vec2ForPreTraining(config).to(torch_device).eval() with torch.no_grad(): outputs_rand = model_rand( inputs_dict.input_values.to(torch_device), attention_mask=inputs_dict.attention_mask.to(torch_device), mask_time_indices=mask_time_indices, ) # compute cosine similarity cosine_sim_rand = torch.cosine_similarity( outputs_rand.projected_states, outputs_rand.projected_quantized_states, dim=-1 ) # retrieve cosine sim of masked features cosine_sim_masked_rand = cosine_sim_rand[mask_time_indices] # a pretrained wav2vec2 model has learned to predict the quantized latent states # => the cosine similarity between quantized states and predicted states > 0.5 # a random wav2vec2 model has not learned to predict the quantized latent states # => the cosine similarity between quantized states and predicted states is very likely < 0.1 self.assertTrue(cosine_sim_masked.mean().item() - 5 * cosine_sim_masked_rand.mean().item() > 0) @unittest.skipIf(torch_device != "cpu", "cannot make deterministic on GPU") def test_loss_pretraining(self): model = Wav2Vec2ForPreTraining.from_pretrained( "facebook/wav2vec2-base", attention_dropout=0.0, feat_proj_dropout=0.0, hidden_dropout=0.0, layerdrop=0.0, ) model.to(torch_device).train() feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained( "facebook/wav2vec2-base", return_attention_mask=True ) input_speech = self._load_datasamples(2) inputs_dict = feature_extractor(input_speech, return_tensors="pt", padding=True) features_shape = ( inputs_dict["input_values"].shape[0], model._get_feat_extract_output_lengths(inputs_dict["input_values"].shape[1]), ) torch.manual_seed(0) mask_time_indices = _compute_mask_indices( features_shape, model.config.mask_time_prob, model.config.mask_time_length, device=inputs_dict["input_values"].device, min_masks=2, ).to(torch_device) with torch.no_grad(): outputs = model( inputs_dict.input_values.to(torch_device), attention_mask=inputs_dict.attention_mask.to(torch_device), mask_time_indices=mask_time_indices, ) # check diversity loss num_codevectors = model.config.num_codevectors_per_group * model.config.num_codevector_groups diversity_loss = (num_codevectors - outputs.codevector_perplexity) / num_codevectors self.assertTrue(abs(diversity_loss.item() - 0.8859) < 1e-3) # check overall loss (contrastive loss + diversity loss) expected_loss = 62.5170 self.assertTrue(abs(outputs.loss.item() - expected_loss) < 1e-3) def test_inference_keyword_spotting(self): model = Wav2Vec2ForSequenceClassification.from_pretrained("superb/wav2vec2-base-superb-ks").to(torch_device) processor = Wav2Vec2FeatureExtractor.from_pretrained("superb/wav2vec2-base-superb-ks") input_data = self._load_superb("ks", 4) inputs = processor(input_data["speech"], return_tensors="pt", padding=True) input_values = inputs.input_values.to(torch_device) attention_mask = inputs.attention_mask.to(torch_device) with torch.no_grad(): outputs = model(input_values, attention_mask=attention_mask) predicted_logits, predicted_ids = torch.max(outputs.logits, dim=-1) expected_labels = [7, 6, 10, 9] # s3prl logits for the same batch expected_logits = torch.tensor([6.1186, 11.8961, 10.2931, 6.0898], device=torch_device) self.assertListEqual(predicted_ids.tolist(), expected_labels) self.assertTrue(torch.allclose(predicted_logits, expected_logits, atol=1e-2)) def test_inference_intent_classification(self): model = Wav2Vec2ForSequenceClassification.from_pretrained("superb/wav2vec2-base-superb-ic").to(torch_device) processor = Wav2Vec2FeatureExtractor.from_pretrained("superb/wav2vec2-base-superb-ic") input_data = self._load_superb("ic", 4) inputs = processor(input_data["speech"], return_tensors="pt", padding=True) input_values = inputs.input_values.to(torch_device) attention_mask = inputs.attention_mask.to(torch_device) with torch.no_grad(): outputs = model(input_values, attention_mask=attention_mask) predicted_logits_action, predicted_ids_action = torch.max(outputs.logits[:, :6], dim=-1) predicted_logits_object, predicted_ids_object = torch.max(outputs.logits[:, 6:20], dim=-1) predicted_logits_location, predicted_ids_location = torch.max(outputs.logits[:, 20:24], dim=-1) expected_labels_action = [0, 0, 2, 3] expected_logits_action = torch.tensor([0.4568, 11.0848, 1.6621, 9.3841], device=torch_device) expected_labels_object = [3, 10, 3, 4] expected_logits_object = torch.tensor([1.5322, 10.7094, 5.2469, 22.1318], device=torch_device) expected_labels_location = [0, 0, 0, 1] expected_logits_location = torch.tensor([1.5335, 6.5096, 10.5704, 11.0569], device=torch_device) self.assertListEqual(predicted_ids_action.tolist(), expected_labels_action) self.assertListEqual(predicted_ids_object.tolist(), expected_labels_object) self.assertListEqual(predicted_ids_location.tolist(), expected_labels_location) self.assertTrue(torch.allclose(predicted_logits_action, expected_logits_action, atol=1e-2)) self.assertTrue(torch.allclose(predicted_logits_object, expected_logits_object, atol=1e-2)) self.assertTrue(torch.allclose(predicted_logits_location, expected_logits_location, atol=1e-2)) def test_inference_speaker_identification(self): model = Wav2Vec2ForSequenceClassification.from_pretrained("superb/wav2vec2-base-superb-sid").to(torch_device) processor = Wav2Vec2FeatureExtractor.from_pretrained("superb/wav2vec2-base-superb-sid") input_data = self._load_superb("si", 4) output_logits = [] with torch.no_grad(): for example in input_data["speech"]: input = processor(example, return_tensors="pt", padding=True) output = model(input.input_values.to(torch_device), attention_mask=None) output_logits.append(output.logits[0]) output_logits = torch.stack(output_logits) predicted_logits, predicted_ids = torch.max(output_logits, dim=-1) expected_labels = [251, 1, 1, 3] # s3prl logits for the same batch expected_logits = torch.tensor([37.5627, 71.6362, 64.2419, 31.7778], device=torch_device) self.assertListEqual(predicted_ids.tolist(), expected_labels) self.assertTrue(torch.allclose(predicted_logits, expected_logits, atol=1e-2)) def test_inference_emotion_recognition(self): model = Wav2Vec2ForSequenceClassification.from_pretrained("superb/wav2vec2-base-superb-er").to(torch_device) processor = Wav2Vec2FeatureExtractor.from_pretrained("superb/wav2vec2-base-superb-er") input_data = self._load_superb("er", 4) inputs = processor(input_data["speech"], return_tensors="pt", padding=True) input_values = inputs.input_values.to(torch_device) attention_mask = inputs.attention_mask.to(torch_device) with torch.no_grad(): outputs = model(input_values, attention_mask=attention_mask) predicted_logits, predicted_ids = torch.max(outputs.logits, dim=-1) expected_labels = [1, 1, 2, 2] # s3prl logits for the same batch expected_logits = torch.tensor([2.1722, 3.0779, 8.0287, 6.6797], device=torch_device) self.assertListEqual(predicted_ids.tolist(), expected_labels) self.assertTrue(torch.allclose(predicted_logits, expected_logits, atol=1e-2))